Response of Potentially Mineralizable Soil Nitrogen and Indices of Nitrogen Availability to Tillage System
Bibliographic record
Abstract
Tillage practices may affect the active fraction of soil organic N. As part of a national project to examine soil management and environmental controls on the active fraction of organic N, this study examined the effects of no‐till (NT) and conventional tillage (CT) systems on the quantity of potentially mineralizable soil N (N 0 ) and mineralizable N pools, and the potential to detect changes in these pools using N availability indices. Preplant soil samples from the top 15 cm were collected from four long‐term tillage experiments at Swift Current, SK; Woodslee, ON; L'Acadie, QC; and Agassiz, BC. Potentially mineralizable N was determined by aerobic incubation at 25°C and periodic leaching for 24 wk. The N 0 was greater under NT than under CT, but only at Swift Current. The labile and intermediate mineralizable N pools were significantly higher under NT than under CT at three of the four sites. The stable mineralizable N pool and the mineralization rate coefficient ( k ) were greater under NT than under CT at only one of the four sites. Adoption of NT influenced the quality of the active organic N fraction at three sites, as indicated by an increased proportion of mineralizable N in the more labile N pools. Among tested indices of N availability, KCl‐extractable NH 4 –N, NaOH‐extractable N, Illinois Soil N Test, phosphate‐borate buffer extractable N, and particulate organic C were most sensitive to tillage‐induced changes in the active organic N fraction. Tillage‐induced changes in the size and quality of the active organic N fraction may influence soil N supply and should be considered in optimizing fertilizer N management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".